In this age of big data, data shortage is not a problem. In actuality, the opposite is the case. Retailers are frequently drowning in their own data, triggering an overwhelming sense of anxiety and disquietude.

Not only that, these companies typically do not have access to the resources than can transform this mess of data into something accessible, actionable, and profitable.

This first installment in our 7 Steps to Marketing Nirvana Series will outline how to eliminate data overload and transform your marketing team into data overlords.

Revitalize Your Customer Portfolio

The general reason most companies’ databases are inadequate, incongruous, and insufficient is that their Point of Sale (POS) systems were developed to solely ingest transactional data, which consists of five key categories:

Who bought? (Sometimes)

What product?

For how much?

When?

Through which channel?

While such information can be useful on its own, this is merely scratching the surface of what is truly possible. The fact is, these rudimentary systems have not been designed with the long-term relationship of a customer in mind.

For instance, POS systems do not track the trails leading from one purchase to another, and also do not attempt to understand the stimuli that incentivize each subsequent purchase. In addition, they are not directly actionable – rendering your data management abilities a far cry from their full potential.

Ingest/Filter Your Customer Transaction Information

Marketing software solutions have evolved dramatically over the past few years – offering small and medium sized businesses capabilities they have never had before. Predictive Marketing Automation (PMA) Software is one such class of software.

In contrast to POS solutions, this type of solution maintains customer databases, predicts the relative value of customers and automates marketing communications.

In turn, the first priority is to get your POS data organized, cleaned, and filtered for the sake of maximizing data hygiene. This is done by uploading all of the records into a PMA’s standard data model.

Think of it as cleaning a messy garage, fine-tuning a car in a body shop, filtering dirty water, assembling puzzle pieces together, or weeding a garden.

Keep in mind, one of the main goals of marketing is to know as much about your customer as possible.

Following ingestion through a standard data model, all of your retail information becomes stitched together across all available channels. This generates the omnichannel, 360-degree customer view that the mega-retailers strive for.

Without such an expansive customer viewpoint, it is impossible to accurately calculate anyone’s customer journey or respective Lifetime Value (LTV).

By simply uploading your customer transactional data into a PMA, you are already well on your way towards developing the comprehensive customer portraits, strategies, and services possessed by the Amazons of the world.

Make no mistake – without this level of comprehensive knowledge about your customer base (along with the ability to manage that knowledge effectively) your chances of succeeding – let alone competing – on a major league level are incredibly slim.

Define and Create Reference Tables

Reference tables are constructed from the various types of transaction codes used to track retail or online sales in POS systems. These typically include customer data, transaction data, and product data.

For example, if a buyer enters a furniture store and purchases a couch on clearance (where the price has been dramatically reduced), that particular item would have a special transaction code indicating the markdown in price.

This simple set of information can be used to form a clearer profile of each buyer. For instance, if the person who bought the couch also purchased other items on sale (either that same day or over a longer period of time) he/she could be categorized as a discount buyer.

Every nuance of each purchase (full-price, discount, coupon, time/date) that is captured by POS systems can be ingested into a PMA and built to form reference tables. Or, in other words, PMA’s ingest transactions codes and provide the magic “decoder ring” to make sense of the data.

Compile and Access Promotion History

One of the most valuable elements of a PMA is the establishment of a promotion history facility. This systematically tracks and deduces how each customer responds to various forms of communication and/or solicitation (email, messaging, offers, etc.).

The digital world we inhabit is a closed system in which any form of online activity can be readily tracked and stored.

Therefore, if your company sends out a promotional email to its customer base, a PMA can tell you what particular strides each recipient made towards a purchase. For instance:

Who opened the message?

Did they click through to the site?

What items did they browse?

Overall, how engaged is the customer with your brand?

Any of these bits of information can offer powerful clues about exactly where each of your customers are along their respective journeys and how to best manage the dialogue (or curriculum of communications (COC)) with them.

For instance, if a customer left an item in their cart, send a simple reminder within the next few days. This little nudge can be the final trigger towards making the actual purchase.

Marketing is all about relevance. Your chances of securing new customers or inciting repeat purchases from your existing base directly correlates to your ability to send the right message, to the right person, at the right time, via the right channel.

Define Your Metrics

Another component of this process is to define the most important set of metrics (also known as Key Performance Indicators (KPI)) that are specific to your business. These are ways of measuring your customer management and overall company performance.

Different cohorts of customers should always be treated differently. Some metrics can be more valuable than others in different contexts – depending upon the terms of the behaviors you are attempting to trigger in your customer base. Therefore, the most pertinent metrics should be defined and consistently evaluated.

Customer Acquisition Cost (CAC)

A crucial metric to evaluate in this context is Customer Acquisition Cost (CAC). CAC is determined by dividing all of the costs spent on obtaining new customers by the actual amount acquired in a particular time frame.

Ultimately, the goal is to minimize your CAC as much as possible by carefully assessing your Return on Investment (ROI) for each customer won.

Lifetime Value (LTV)

CAC should always be measured in tandem with another important metric – Customer Lifetime Value (LTV). This determines how much a customer is worth to you over their lifetime.

Customer value metrics can also be assessed in relation to a number of different factors and time frames (i.e. 1-year, 2-year, historical, etc.)

Historical Value

A simple metric is to examine how much a how much money a customer has spent in the past over various time frames.

For instance, say that a frequent flier has flown 1 million miles with an airline over his lifetime, but now no longer flies as much as he used to. Meanwhile, another customer has flown less overall (say 500,000) but much more within the past year.

Despite the fact that the former has flown with the airline twice as much overall, he would likely not receive the same degree of perks as the the latter. This is because the frequency of recent miles outweighs the overall number of miles over a longer period of time.

Projected Future Value

Projected future value (or potential value) is a key metric that is often overlooked. If you blindly assume that every customer has the same potential value, then you are likely missing out on a range of opportunities.

You should always aim to recognize customers for what they’ve done in the past while also incentivizing future behaviors leading to more subsequent purchases.

For instance, if you do not know what a target or cohort’s potential is, how would you be able to truly define your level of marketing success? Your company should have the ability to answer a few key questions:

Are we achieving the full potential of each type of customer that we have in our database?

How much should we invest in each customer in the future? Via which channel(s)?

The bottom line is that a PMA system can evaluate both historical and current customer behavior to calculate the respective value of any customer down the road. This is an easy, accessible way for marketers to justify marketing expenditures by using the software to show their finance departments exactly how investments in particular omnichannel campaigns are delivering the required ROI.

Category Value

Category value is a calculation of how much and how frequently a customer spends on your particular product or service in proportion to others..

While many marketers would love to have a clear, accurate view of category value, they often lack the resources and technology to do so.

Let’s go back to the airline example. Say a customer flies a total of 100,000 miles per year, but only travels 25,000 with your airline. A PMA can dive into survey data, cluster/cohort analysis, and gaps between activity and explain why that particular customer is spending their money elsewhere.

With this beneficial knowledge at your fingertips, you can tailor your interactions and offers to that customer in order to win back a greater percentage of their buying potential while gaining an edge on your competition.

Conclusion

By this point, your customer and transactional data have been uploaded into PMA software, where it has been systematically organized and filtered.

Not only do you now have a clean garage, you also have a well-oiled, fully fueled vehicle ready to hit the road on your path to Marketing Nirvana. But in order to get there, you need a roadmap identifying who your customer base is, and where to find them.

Gary’s background includes over 30 years of analytics & database innovation for several leading Fortune 500 companies and Madison Avenue advertising agencies. Gary has been a frequent lecturer and author on the topics of database marketing and applied statistics. His articles have been published in DM News, Direct Marketing and the Journal of Direct Marketing. He recently was President of the Direct Marketing Idea Exchange and served on their Board. Gary received his M.S. in Industrial Administration from Carnegie Mellon University.